System Identification Ice 6th Sem Syllabus for BE 2017 Regulation Anna Univ (Professional Elective I) detail syllabus for Instrumentation & Control Engineering (Ice), 2017 regulation is collected from the Anna Univ official website and presented for students of Anna University. The details of the course are: course code (IC8072), Category (PE), Contact Periods/week (4), Teaching hours/week (2), Practical Hours/week (2). The total course credits are given in combined syllabus.
For all other ice 6th sem syllabus for be 2017 regulation anna univ you can visit Ice 6th Sem syllabus for BE 2017 regulation Anna Univ Subjects. For all other Professional Elective I subjects do refer to Professional Elective I. The detail syllabus for system identification is as follows.
Course Objective:
- To understand the mathematical modelling of systems.
- To observe systems by their behaviour using Parametric Identification methods using online and offline Datas
- To observe systems by their behaviour using Nonparametric Identification Methods using Online and Offline Datas
- To estimate and validate the datas using parametric and recursive estimation methods
- To perform case studies on electromechanical and process control systems
Unit I
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Unit II
Parametric Indentification
Steps in identification process, determining model structure and dimension, Linear and nonlinear model structures (ARX, ARMAX, Box-Jenkins, FIR, Output Error models), Input signals: commonly used signals, spectral properties, and persistent excitation, Residual analysis for determining adequacy of the estimated models.
Unit III
Parametric Estimation
Linear regression, least square estimation, statistical analysis of LS methods, Minimizing prediction error- identifiability, bias, Least squares, relation between minimizing the prediction error and the MLE, MAP, Convergence and consistency, asymptotic distribution of parameter estimates, Instrumental Variable Method.
Unit IV
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Unit V
Case Studies
Electro Mechanical Systems, Process Control Systems using Matlab/Equivalent System Identification Toolbox.
Course Outcome:
- Be familiar with different model structures, parameterization, identifiability, structure determination and order estimation
- Be able to perform parameter estimation using different identification techniques
- Be able to identify plants online using recursive estimation methods
- Be able to set up an experiment, identify a nominal model, assess the accuracy and precision of this model,
- Be appropriate design choices to arrive at a validated model.
References:
- jung, L. System Identification: Theory for the User, 2nd Edition, Prentice-Hall, 1999, ISBN 0-13656695-2.
- Torsten Soderstrom, PetreStoica, System Identification, Prentice Hall International (UK) Ltd. 1989.
- Karel J. Keesman, System Identification, An introduction, Springer, 2011.
- Zhu, Y. Multivariable System Identification for Process Control, Pergamon, 2001.
- Landan ID, System Identification and Control Design, Prentice Hall
- ArunK.Tangirala,Principles of System Identification: Theory and Practice,CRC Press,2014.
For detail syllabus of all other subjects of BE Ice, 2017 regulation do visit Ice 6th Sem syllabus for 2017 Regulation.
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